Research Of Setting Condition And Merging Control Method For CAV Dedicated Lanes In Mixed Traffic

With the development of intelligent and networked automobile industry,the traffic flow will be composed of vehicles with different degrees of intelligence.This kind of traffic environment is called mixed traffic environment.As the future development form of automobiles,the share of connected and autonomous vehicles is gradually increasing in the traffic composition.In mixed traffic scenarios,it is believed that setting up special lanes for connected and autonomous vehicles can reduce the conflicts between vehicles with different degrees of intelligence and improve traffic efficiency.However,the existing research on the change mechanism of road capacity under the dedicated lane scenario is insufficient,and the critical point of the increase of road capacity has not been fully studied.Secondly,there are few researches on the control methods of connected and autonomous vehicles in the mixed traffic lane scenario,and it is urgent to design a vehicle control strategy suitable for the characteristics of connected and autonomous vehicles.In this paper,the reduction mechanism of capacity under different right-of way allocation strategies is studied,the influence of dedicated lanes for connected and autonomous vehicles on traffic flow is analyzed,and the applicable traffic conditions are quantitatively analyzed to make full use of road resources and improve traffic efficiency.Secondly,this paper studies the merging control method of CAV.Lane changing characteristics of intelligent networked vehicles under mixed traffic lane scenarios are analyzed,lane changing scenarios are built,lane changing trajectory equations are established,track sets are generated,and the optimal lane changing trajectory is determined through multi-objective optimization.Finally,a dynamic multi-vehicle confluence control method based on game theory is proposed.The main research contents are as follows:Firstly,the design of model parameter estimation method,the paper analyses the mixed traffic scenarios road traffic capacity and vehicle model related parameters,the car road location,the relationship between the intelligent car snatched permeability,and then based on the markov chain design the lanes scenarios road traffic capacity estimation method,and analyzed the advantage of the model,finally the simulation results verify the validity of the prediction estimation model.Secondly,considering the lane changing characteristics of intelligent networked vehicles in mixed traffic lane scenarios,a lane changing model of intelligent networked vehicles based on multi-objective optimization was established.A polynomial trajectory was established to plan the lane changing trajectory of the intelligent networked vehicle,and multiple track clusters were generated.At the same time,considering the safety constraints in the lane changing process,the optimal trajectory was found from the target of lane changing efficiency,comfort level and lane changing fuel consumption.The simulation experiment is carried out to verify the effectiveness of the proposed method.Finally,in the scenario of a dedicated lane,the characteristics of the confluence process of connected and autonomous vehicles are considered,and the characteristics of information exchange and sharing are targeted at the collaborative control process between connected and autonomous vehicles and front and rear vehicles in the confluence process.Firstly,the revenue function of vehicle lane changing based on game model is constructed,which is taken as the objective function of optimal control while taking into account the goal of vehicle driving safety.Based on this,the optimal control method is designed to comprehensively consider the optimal revenue.Finally,the experimental results show that the proposed method can control vehicle confluence and improve the efficiency of the confluence process.